Causal normalizing flows: from theory to practice

• URL: http://arxiv.org/abs/2306.05415v2
• Date: Fri, 8 Dec 2023 14:29:47 GMT
• Title: Causal normalizing flows: from theory to practice
• Authors: Adri\'an Javaloy, Pablo S\'anchez-Mart\'in and Isabel Valera
• Abstract summary: We use recent results on non-linear ICA to show that causal models are identifiable from observational data given a causal ordering. Second, we analyze different design and learning choices for causal normalizing flows to capture the underlying causal data-generating process. Third, we describe how to implement the do-operator in causal NFs, and thus, how to answer interventional and counterfactual questions.
• Score: 10.733905678329675
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we deepen on the use of normalizing flows for causal reasoning. Specifically, we first leverage recent results on non-linear ICA to show that causal models are identifiable from observational data given a causal ordering, and thus can be recovered using autoregressive normalizing flows (NFs). Second, we analyze different design and learning choices for causal normalizing flows to capture the underlying causal data-generating process. Third, we describe how to implement the do-operator in causal NFs, and thus, how to answer interventional and counterfactual questions. Finally, in our experiments, we validate our design and training choices through a comprehensive ablation study; compare causal NFs to other approaches for approximating causal models; and empirically demonstrate that causal NFs can be used to address real-world problems, where the presence of mixed discrete-continuous data and partial knowledge on the causal graph is the norm. The code for this work can be found at https://github.com/psanch21/causal-flows.

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